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Found 6 papers, 2 papers with code
Data Augmentation for Modeling Human Personality: The Dexter Machine
Modeling human personality is important for several AI challenges, from the engineering of artificial psychotherapists to the design of persona bots.
Revisiting Information Cascades in Online Social Networks
We design several algorithms for this task, ranging from a simple greedy algorithm that only learns $u$'s conditional probability distribution, ignoring the rest of $V$, to a convolutional neural network-based algorithm that receives the activity of all of $V$, but does not receive explicitly the social link structure.
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms
We propose a new method for classifying reviewers as spammers or benign, combining machine learning with a message-passing algorithm that capitalizes on the users' graph structure to compensate for the possible scarcity of labeled data.
STEM: Unsupervised STructural EMbedding for Stance Detection
Our framework is unsupervised and domain-independent.
A greedy anytime algorithm for sparse PCA
The taxing computational effort that is involved in solving some high-dimensional statistical problems, in particular problems involving non-convex optimization, has popularized the development and analysis of algorithms that run efficiently (polynomial-time) but with no general guarantee on statistical consistency.
Do semidefinite relaxations solve sparse PCA up to the information limit?
In fact, we conjecture that in the single-spike model, no computationally-efficient algorithm can recover a spike of $\ell_0$-sparsity $k\geq\Omega(\sqrt{n})$.
